Oil Price Shocks: Bank Size and Firm Size Effects. Abstract

Transcription

1 Oil Price Shocks: Bank Size and Firm Size Effects Abstract This paper examines the influence that regional variation plays in the propagation of oil price shocks using a state-panel vector autoregression model. Personal income and housing price impulse responses are computed for each of the lower 48 states and then regressed onto various state characteristics reflecting average bank size and firm size. Our research contributes to the literature in several ways. First, we find that personal income and housing price impulse responses are amplified or dampened by oil price shocks depending on the size distributions of banks and firms within a state. Second, and more importantly, the small bank and small firm size effects normally thought to be associated with the propagation of monetary policy shocks, are shown to propagate oil price shocks. Third, we find strong and distinct patterns in the data related to bank and firm size. Lastly, the asymmetric responses and patterns between personal income and housing prices and bank size and firm size are indicative of multiple transmission channels. Keywords: Oil price shock; small bank; small firm; transmission; regional; monetary policy JEL: R11, Q43, E52, G21 0

2 Regional economic theory, supported by empirical research, indicates that not all regions within the United States respond the same way to macroeconomic shocks. Regional variation can amplify or dampen shocks and have the potential to explain the cross-sectional dispersion observed in economic activity across the country. The purpose of this paper is to gain a better understanding of the transmission mechanisms behind oil price shocks by identifying the local factors that account for different regional economic responses. The rich diversity across the states, combined with the volatility in oil prices in recent years, makes this area of research topical. Bernanke, Gertler, and Watson (1997) contend that although oil price shocks may have instigated many of the U.S. recessions since World War II, it was actually the Federal Reserve s policy reaction to the shocks that accounts for most of the economic downturns. When the U.S. experienced a positive oil shock, the Federal Reserve raised interest rates to mitigate the shock s inflationary potential and an economic downturn followed. The Federal Reserve, in monitoring and reacting to oil prices, influences the economic activity, which in turn, influences the demand for oil. Hamilton and Herrera (2004), on the other hand, argues that oil shocks are the primary source of most of U.S. recessions and that the Federal Reserve played a much smaller role. These arguments suggest that when examining the transmission mechanisms for oil price shocks, monetary policy variables should be included endogenously in the system. We conduct this study on the lower 48 states with national and state data from 1991 to Macroeconomic data include crude oil prices, the federal funds rate, and the consumer price index. The state variables employed are per capita personal income and residential housing prices. Our procedure involves three steps. First, we estimate a state-panel VAR with these variables. Second, we estimate personal income and housing price impulse responses from an oil price shock for each state. Third, we regress the impulse responses onto state characteristics. Borrowing ideas developed in the monetary policy literature, our primary interest lies in the estimated slope coefficients for bank-size and firm size. From these estimates, we can determine which characteristics dampen, propagate, or have no effect on personal income and housing prices after an oil price shock. The value to employing a regional VAR is that it allows for difference state reactions to oil as well as monetary policy shocks. 1

3 Different states have different demand and supply exposures to oil prices. On the demand side, northern states might react more negatively to an oil price increase than southern states. On the supply side, oil producing states such as Texas, Oklahoma, Louisiana, and Wyoming might see personal income increase when oil prices increase. This paper enriches a vast body of energy economic research by examining the transmission mechanisms of oil price shocks within a framework typically used to analyze monetary policy transmission. Our contributions to the literature are several. First, by employing the size distributions of banks and firms across the lower 48 states, we find a distinct difference between the personal income impulse responses of states with a greater presence of small banks and those states with a greater presence of large banks. 1 Second, housing prices, in response to an oil price shock, are shown to vary conditionally on bank size across the states. Third, because of the distinct differences and patterns in the way personal income and housing prices respond to oil price shocks conditional on entity size, it suggests different channels are at work in the transmission processes. Lastly, we conduct simultaneous monetary policy and oil price shocks and find that the addition of a monetary policy shock changes the response patterns across the states very little compared to an oil price shock alone. 2 Carlino and DeFina (1998) develop the primary framework used in our paper, connecting monetary policy and regional variation. With nearly two decades of addition research at our disposal, however, we refine the methodology, expand the model s predictive capacity, and augment the dataset in the following ways. First, we recast their VAR to handle oil price shocks, instead of just federal funds rate shocks. Second, Carlino and DeFina (1998) estimate their VARs in first differences, losing some of the information embedded in the levels of the data. Sims (1980) and Sim, Stock, and Watson (1990) warn about the hazards of transforming nonstationary data in VARs. 3 Thus, our VARs-in-levels 1 Less than 500 employees is a widely used standard for defining a small firm according to the Small Business Association (SBA). Bank size data is highly skewed. We define small banks as those within the 75th percentile. 2 This finding is not necessarily inconsistent with Bernanke, Gertler, and Watson (1997) who examine the efficacy of monetary policy on a macro level. Our approach focuses on regional characteristics that may influence shocks, it does not speak to macroeconomic effectiveness of monetary policy. 3 Indeed, unit root tests are notorious for suffering from low power, and estimating an unknown cointegrating mechanism is subject to misspecification error, see for example Cochrane (1991) and Campbell and Perron (1991). On page 136 of Sims, Stock, and Watson (1990), This work shows that the common practice of attempting to transform models to 2

4 include potentially important stochastic trends. Third, we expand and partition the data to include banks from each total asset size quartile, and firms in various size categories from less than 20 employees up to 500 or more. Lastly, we also add state housing prices to the VAR. We then regress the impulse responses from these shocks onto state data following Carlino and DeFina (1998). When we shock oil prices upward by one standard deviation, we find statistically significant evidence of bank size and firm size effects, each with the theoretically correct sign, positive. In other words, the personal income of states with a disproportionate share of small banks and small firms are more sensitive to oil shocks than other states. These results suggest that the small bank and small firm effects, which were developed to explain the propagation of monetary policy, are capable of explaining the propagation of oil price shocks. We perform several robustness checks by addressing concerns about possible outliers by removing states from the sample with a high amount of gross state product related to oil and gas extraction, by estimating the results with a federal funds rate that is adjusted for the zero lower bound, and by recasting the VAR in first differences. Regardless of these checks, the patterns among personal income and housing prices across the states, as they relate to the distribution of bank size and firm size presented in this paper remain unchanged. The remainder of this paper unfolds as follows: section I reviews the relevant literature on monetary shocks, oil price shocks, transmission channels, and their relationship to regional variation. Section II presents our panel-var model that incorporates oil prices, federal funds rate, consumer prices, personal income, and housing prices. Section III reviews our expectations and empirical findings. Section IV concludes the paper. I. Literature Review Among the many channels proposed in the literature for the transmission of monetary policy, we begin our review by focusing on three: the balance sheet channel, the bank lending channel, and the working capital channel. Although these channels are relevant for all banks and firms, regardless of size, the stationary form by differencing or cointegration operators whenever it appears likely the data are integrated is in many cases unnecessary. 3

5 literature often discusses them in the context of small banks and small firms. Most explanations of monetary policy efficacy rely on frictions or rigidities to magnify and propagate shocks. Smaller entities are a natural place to look. We next discuss the oil price shocks literature. The literature review on oil price shocks is brief, as there is very little work relating oil price shocks to bank size and firm size. Consequently, we relate the frictions and rigidities found in the small bank and small firm monetary shock literature to oil price shocks. Finally, while regional variation is a broad concept, with a seemingly endless number of dimensions, we narrow the discussion to a few characteristics commonly found in the regional economic literatures. i. Monetary Policy Transmission The balance sheet describes a process where the value of a firm s balance sheet, and hence its net worth, adjusts to changes in interest rates caused by a shift in monetary policy, as in Gertler (1992), Bernanke, Gertler, and Gilchrist (1996), and Peek and Rosengren (2013). There are two reinforcing phenomena that account for the change in firm net worth and we ll discuss both of them in terms of a contractionary monetary policy. First, a contractionary policy leads to higher interest rates and a decline in the present value of firm future cash flow causing net worth to shrink. In an economy with asymmetric information, firm net worth is a proxy for collateral value suggesting the ability to obtain financing is negatively affected. Second, a monetary contraction leading to higher interest rates dampens economic activity and reduces economic spending leading to lower cash flow. Changes in monetary policy also influence the economy via the bank lending channel as discussed in Kashyap, Stein, and Wilcox (1993), Bernanke and Gertler (1995), and more recently in Peek and Rosengren (2013). The bank lending channel focuses on the supply of overall credit available in the economy. When the Federal Reserve decreases the money supply through open market sales of securities, it drains reserves from the banking system. A loss in reserves ultimately causes a decline in checking and savings deposits. To the extent that banks cannot make up these lost deposits through more expensive time deposits, such as CDs, the supply of overall loanable funds decreases causing bank-dependent borrowers to reduce output as they are unable to finance their recent pace of business activity. If, on the other hand, banks are able to raise additional funds by way of more expensive time 4

6 deposits, the increased cost of borrowing ultimately reduces the amount banks lend. This can be represented as an inward shift in the supply of loanable funds. Regardless of the funding situation of the bank, the bank lending channel predicts higher interest rates and less lending due to changes in banks funding sources. The working capital channel suggests that because firms borrow the cost of production and then use sales proceeds to pay off loans, they are vulnerable to a rise in interest rates, see Barth and Ramey (2001), Ravenna and Walsh (2006), Christiano, Trabandt, and Walentin (2010). The effect is magnified by a contractionary monetary policy shock that reduces sales and causes an accumulation of inventory costs. Production is then reduced causing the economy to contract. The theory behind these channels is independent of entity size. However, a number of researchers argue that because small banks and small firms are subject to constraints, which magnify the frictions associated with the channels, small entities have the potential to magnify shocks. Gertler and Gilchrist (1993, 1994) claim that small firms are especially prone to a contraction in monetary policy. Higher interest rate increase carrying costs, and in response, small firms quickly reduce inventory levels thereby lowering their financing needs to support it. At the same time, consistent with Kashyap and Stein (1995), cash flows, net of higher interest expense, are reduced for loan-dependent firms further deteriorating the balance sheet. These affects can be represented as an inward shift in the demand curve for loanable funds. Consistent with these ideas, Gertler and Gilchrist (1994) find that loans to small manufacturing firms quickly drop after a contractionary monetary shock. For a bank with a flexible capital structure, a contractionary monetary shock drains its reserves, which in turn, causes the bank to realign its funding sources away from high reserve deposits to low reserve funding sources such as large CDs or commercial paper, without friction. Thus, outstanding and potential new loans are relatively insulated from the policy shock. However, smaller banks are prone to liquidity frictions that make it difficult for them to freely adjust to monetary shocks. According to Kashyap and Stein (2000) small banks do not have easy access to these alternative sources of funds. Small banks, as a result of information asymmetries and possibly diseconomies of scale, are unable 5

7 access the capital markets and their loan portfolios are likely to shrink. A contraction in monetary policy, therefore, leads to a contraction in lending by smaller banks and less economic activity. Kashyap, Lamont, and Stein (1994) argue that small businesses account for much of the decline in economic activity following a Federal Reserve tightening and that there are a number of reasons for the contraction. First, small firms are highly reliant on bank loans. Second, small firms are often highly specialized and operate in narrow markets. Their inventory and equipment are idiosyncratic resulting in illiquid collateral and limiting their access to credit. Lastly, there are higher agency costs that are associated with monitoring small idiosyncratic firms. Higher agency costs coupled with higher interest rates from a monetary contraction, substantially raises the cost of borrowing and reduces loan demand. In response to a tightening in monetary policy, small businesses are likely to reduce their dependence on external financing by shrinking inventory levels as discussed in Gertler and Gilchrist (1993, 1994). Inventory reductions lead to a slowdown in production, hours worked, employment, and ultimately worker income, especially for regions with a significant proportion of small businesses. Berger et al. (2005) examine bank organizational structure of as it relates to both bank and firm size. They find that small firms, which often do not have audited financial statements, and in some cases, no financial statements at all, are more likely to conduct business with small banks on a more personal level. Such small firms maintain only soft information, rather than more reliable hard information such as financial statements. The authors argue that large hierarchy-driven banks have a difficult time processing and relaying soft information up the chain of command for loan approval. Small banks, on the other hand, are relatively hierarchically flat with the president often responsible for extending loans. Berger et al. (2005) find that these small bank-small firm relations are maintained over relatively short physical distances where frequent monitoring in conjunction with relationship-building is continual. The high fixed-costs associated with establishing a close relationship built around soft financial information tends to make long term, rather than short term, relationships more profitable. Monetary policy shocks do not necessarily occur in isolation to other shocks and in some cases they may be a reaction to a stock market shock or even an oil price shock. Given the Federal Reserve s 6

8 mandate of full employment and stable prices, an oil price shock could induce the Federal Reserve to react by attempting to offset the consequences. Bernanke, Gertler, and Watson (1997) examine the monetary responses to oil price shocks over several decades and find that most of the negative impact on economic activity coming from an oil price increase is actually the result of the Federal Reserve s response to counteract the oil shock. In other words, they claim that it is the combination of oil price shocks and the ensuing contractionary monetary policy response that accounts for the large negative impact that many others have suggested is coming from oil price shocks alone. ii. Oil Shock Transmission Interestingly, to the best of our knowledge, the small bank and small firm effects which are prevalent in the monetary policy research are not directly addressed in the oil shock transmission literature. Kilian (2008, 2014) and Brown and Yucel (2002) conduct extension literature reviews of oil price shocks and there is no mention of a small bank or a small firm effect. Nevertheless, there are allusions in the literature even if they are macroeconomically oriented, that may shed light on the subject. For instance, Bernanke (1983) discusses the possibility that the uncertainty following an oil shock could cause firms to postpone investment decisions. We can extend this line of reasoning one step further and suggest that since small firms are less flexible and face more constraints, they may react by reducing investment more than large firms. If small firms tend to conduct business with small banks, as in Berger et al. (2005), then small firms and small banks have the potential to propagate oil price shocks. In a similar vein, Bernanke, Gertler, and Gilchrist (1996) suggest that credit market imperfections resulting from information asymmetries and agency costs may accelerate shocks by increasing the possibility of financial distress and bankruptcy. As a consequence, the higher probability of loan default causes banks to reduce lending, thereby squeezing businesses and propagating shocks. Again, since small banks and small firms often face the brunt of these market imperfections, a larger proportion of small banks and small firms within a state more are likely to propagate shocks. Oil price shocks lead to widespread economic uncertainty as oil is ubiquitous. Firms, knowing that consumers tend to reduce consumption and increase savings in response to an oil price shock, will tend 7

9 to reduce inventory levels consistent with Gertler and Gilchrist (1993, 1994). Inventory is often financed with bank loans. Small firms, especially ones with limited financial documentation according to Berger et al. (2005), tend to conduct business with small banks. While monetary shocks and oil price shocks are different, the underlying sources for the amplification mechanisms might be similar. Could the sources be coming from small banks and small firms? II. The Model and Data In the first step of our modeling process we estimate a VAR for each state. Equations 1 through 5 show the VAR for each state i: FF = c 1,i + 12 s=1 FF t s + 12 s=1 Y i,t s s=1 H i,t s + 12 s=1 Oil t s + 12 s=1 CPI t s + trend + e i,t (1) Oil t = c 2,i + 12 s=1 FF t s + 12 s=1 Y i,t s s=1 H i,t s + 12 s=1 Oil t s s=1 CPI t s + trend + e i,t (2) CPI t = c 3,i + 12 s=1 FF t s + 12 s=1 Y i,t s s=1 H i,t s + 12 s=1 Oil t s s=1 CPI t s + trend + e i,t (3) The equations for state personal income and housing prices take the form: Y i,t = c 4,i + 12 s=1 FF t s + 12 s=1 Y i,t s + 12 s=1 H i,t s + 12 s=1 Oil t s + 12 s=1 CPI t s + trend + e i,t (4) H i,t = c 5,i + 12 s=1 FF t s + 12 s=1 Y i,t s + 12 s=1 H i,t s + 12 s=1 Oil t s + 12 s=1 CPI t s + trend + e i,t (5) where FF denotes actual federal funds rate, Yi denotes state i s log real per capita personal income, Hi denotes state i s log real housing price index, Oil denotes the log price of crude oil, and CPI denotes the Consumer Price Index excluding food and energy. We include crude oil prices as an endogenous variable for this U.S. economic model, consistent with the arguments put forth by Bodenstein, Guerrieri, and Kilian (2012). Each equation for a particular state has identical regressors, and therefore, is estimated by OLS without loss of efficiency, see Judge et al. (1988). Each stochastic variable is lagged 12 months. We ran Dickey-Fuller unit root tests on the residuals, with a trend and 6 lags, on each of the five variables for each state and all results rejected the null hypothesis of a unit 8

10 root with each t-test statistic greater than 5.46 (housing price residual for Washington state is 5.468) in absolute value, strongly suggesting stationarity. The critical value at the five percent level is 3.43 in absolute value. The sources for our dataset are discussed in the Data Appendix to this paper. In the second step of our modeling process we apply positive one standard deviation shock to the price of oil. We employ generalized impulse responses developed by Pesaran and Shin (1998) so that our results are not order sensitive. The third step to our modeling process is the main focus of our paper. Using the personal income and housing price impulse responses for all 48 states, we run a regression, one for each combination of bank size and firm size along with other (conditioning) regional variables. We are especially interested in the results for smaller banks and smaller firms and whether they show evidence of amplifying personal income and housing price after a shock. Our literature review indicates that these entities are likely to experience more information asymmetry, constraints, and frictions in attempting to adjust to shocks, and therefore, we are our main interest. We sort banks into four quartiles based on total assets with cutoffs determined at the national level (lower 48 states). By partitioning the entire size distribution we are better able to compare and contrast the results based on bank size. For all banks within a state that fall into a particular asset quartile, we compute the percentage of loans within the state associated with these banks. Thus, there are 48 loan-based data points, one for each state in each quartile. These data represent our bank size variable. Because the overall distribution is highly skewed, banks in the first three quartiles are considered smaller banks. Bank size data comes from the FDIC. Firm size is measured using the percentage of employment within a state for firms with 20 or less employees, employees, employees, and 500 or more employees. Financial institutions and insurance firms are excluded from the firm size data. Firms with less than 500 employees are typically considered small by the Small Business Association and we adopt their definition. III. Expectations and Estimates i. Expectations Ideally, the slope estimates on bank size and firm size should both be positive for personal income and housing price impulse responses for smaller banks and smaller firms. We hypothesize that given the 9

11 constraints faced by small banks and small firms, a positive oil price shock is likely to propagate personal income and housing prices in states with a high percentage of these entities. For large banks and firms, we expect zero slope estimates suggesting no relationship to an oil price shock. Also included in our oil price shock impulse regressions are several industry mix variables such as the percentage of gross state product (GSP) devoted to the oil and gas extraction sector and the transportation sector. The personal income and housing price impulse responses for states with a large component of production devoted to oil and gas extraction should be more sensitive to a positive oil price shock than states with little resources in oil and gas extraction. Thus, the expected positive sign applies to both personal income and housing prices. The transportation sector represents the business of moving goods and people. A significant component of its cost, directly or indirectly, derives from oil consumption. We expect the personal income and housing price impulse responses of states with a large share of GSP devoted to transportation to be more sensitive (positive sign) to an oil price shock. We do not have strong convictions for the expected signs pertaining to government size from an oil price shock. ii Estimates The average state generalized impulse response for personal income and housing prices from an oil price shock are graphed over each of the 36 steps (months) in Figure 1. As can be seen, personal income drops, as expected, and appears to level off around 36 months after the shock. Housing prices initially drop, as expected, bottom around months 17-24, then rebound a bit but remain negative after 36 months. Thus, a permanent impact on personal income and housing prices is likely. Tables 1 and 2 show the results of the generalized impulse responses for the lower 48 states regressed onto a constant and various state characteristics that have the potential to propagate or dampen oil price shocks. Table 1 shows the full regression results for a positive one standard deviation oil price shock. In the top left panel for personal income, the signs on both bank size and firm size coefficients early in the forecasts are positive and statistically significant, as hypothesized, at the 5 percent level or better. It suggests that the personal income of states with smaller banks and smaller firms, are more 10

12 sensitive to (and propagate) oil price shocks. Interestingly, on the right side of the table for housing prices, bank size coefficients are all negative and statistically significant. It suggests that the housing prices of states with a larger loan portfolio for banks in the percentile asset range are more sensitive to an oil shock than states with a smaller loan presence. In other words, within this bank size category, larger banks tend to propagate oil price shocks more than smaller banks. This is a surprise as we expected a positive and significant relationship in this relatively small bank quartile. The personal income and housing prices of states with a higher percentage of gross state product devoted to oil and gas extraction tend to propagate an oil shock as expected as can be seen from the positive and significant coefficients. The adjusted R 2 s on personal income are substantially larger than those on housing prices early in the forecast horizon. The bottom panel of Table 1 shows regression results for larger banks and larger firms. Interestingly, the coefficients on bank size for the personal income impulses on the left are not significant, and on the right, firm size estimates are not significant for housing price impulses. Table 2 shows more results for an oil price shock for impulses at the 16-month horizon with all four bank asset-size quartiles and four firm size categories. There are strong observable patterns for the bank size and firm size coefficient estimates throughout the table. The regressions include the same variables as in Table 1, but we focus on displaying only estimates for the intercept, bank size, firm size, and adjusted R 2 s because of space constraints. Starting at the top, the intercept estimates are typically negative and significant, and along the bank size row from left to right the slope estimates are positive (negative) and significant for personal income (housing prices), but dwindle in absolute value before turning negative (positive) for large banks in the last column. The negative slope estimates on bank size for housing prices are surprising as we expected a positive relationship. For large banks (right most column), the positive bank size slope estimates suggest that largest banks in this category dampen oil price shocks. The estimates are positive for personal income and housing price responses for all firm size estimates going down the table until we get to the last block of estimates for firms with 500 or more employees. Here the signs flip negative. The pattern gleamed from this table for personal income and housing responses is that firm size slope estimates tend to be positive, but turn insignificant or negative as banks and firms get larger. The consistent housing price sign flip-pattern moving from 11

13 smaller to large banks is puzzling as we expected regions with larger banks and less operating constraints, to adjust to oil price shocks in a way that does not affect local lending and economic activity. The relationships between the slope sign estimates and shock amplification and dampening, discussed above, are hard to visualize working just with tables of data. A more intuitive grasp of relationships may be obtained from Figures 2 through 5. In these figures, the distribution of the 48-state impulse responses for selected columns and rows of Table 2 are presented and unconditional OLS fitted lines are superimposed. The x-axis of each graph in Figure 2 represents the percentage of loans within each state from banks classified in that asset quartile. The y-axis represents the personal income response at step (month) 16 for each of the lower 48 states. The slopes of the fitted lines are consistent with the signs of the coefficient estimates shown in the appropriate column or row of Table 2. 4 An important observation to note in both graphs of Figure 2 is that 11 states personal income (Delaware, Iowa, Kansas, Louisiana, Montana, Nevada, North Dakota, Oklahoma, South Dakota, West Virginia, and Wyoming) respond positively to a positive oil price shock. The upward sloping line, starting below zero, in Figure 2 for the smaller bank quartile (25-50th) indicates the personal income of states with a low percentage of loans within these banks are more sensitive to an oil price shock. In other words, smaller banks tend to propagate an oil price shock. The positive slopes are consistent with the estimates 7.334, 6.349, 6.914, and found in the third column in Table 2. The downward sloping line for the largest banks indicate the personal income of states with a high percentage of loans within large banks are, on average, more sensitive to an oil price shock. Again, we expected a flat slope, indistinguishable from zero, for the largest bank category. The slope is consistent 4 The intercepts in Table 2 are generally, but not always, consistent with those in the figures. The data used in the figures span an entire column or row in the tables. The OLS fitted lines shown in Figures 2 through 5 are based on unconditional estimates as there are no other regional variables employed in the estimation. The intercept and slope estimates in the tables, however, are conditional on the estimated coefficients on the other regional variables included in the regressions. We view the overall consistency between the conditional and unconditional intercept and slope estimates is a form of robustness check. The other conditioning variables are not affecting our conclusions concerning bank size and firm size. 12

14 with the negative coefficient estimates , , -1,959, and in second to last column of Table 2. Overall, both graphs in Figure 2 indicate the personal income of states with a disproportionate amount of small and large banks (the tails of each state s bank asset size distribution) tend to fall more in response to an oil price shock. Based on Table 2, there is a much harder to detect, or in some cases no apparent, relation between the personal income of states and the percentage of loans from banks in the 50-75th percentile (figure not shown). Overall, the results in Tables 1-2 indicate personal income and housing prices respond differently to monetary policy shocks and oil price shocks. The clear bank size and firm size patterns suggest that there are multiple channels at work that impact personal income and housing prices differently. In Figure 3, given the negative intercept combined with the downward sloping line in the small bank quartile (25-50 th ) indicates the housing prices of states with a low percentage of loans within smaller banks are less sensitive to an oil price shock. These lines are consistent and supported by the negative coefficient estimates found in the Table 2 presenting regression results, , , , and Only four states (California, New Mexico, and Nevada) responded with positive impulses for housing prices. On the right, the negative intercept and the upward sloping line for the largest banks in the large bank quartile indicates the housing prices of states with largest banks have a smaller reaction to an oil price shock. Again the results are supported by the positive banks size coefficient estimates in Table 2, the last column. In Figure 4 on the left, given the negative intercept and positive slope, the personal income of states with a low percentage of state employment within smaller firms (20-99 employees) are more sensitive to an oil price shock. The personal income of states with a high percentage of state employment from large firms (500 or more employees) amplify an oil price shock, based on positive slope noted on the right side of Figure 4. As can be seen in Figure 5, the housing prices of states with a low percentage of state employment within small firms (20-99 employees) are more sensitive to, in other words propagate, an oil price shock. Surprisingly, the housing prices of states with a high percentage of state 13

15 employ from large firms (500 or more employees) are more sensitive to an oil price shock. The signs of the intercept and slope coefficient shown in Table 2 are consistent with the fitted lines in Figures 4 and 5. Following the arguments of Bernanke, Gertler, and Watson (1997), we conduct a simultaneous or dual shock to the state VARs: a 50-basis point positive federal funds shock and a one standard deviation positive oil price shock on all 48-lower states. Regression results for the dual shock are shown in Table 3. Table 3 results are not much different from Table 2 indicating monetary policy shocks had little impact on our impulse responses in relationship to bank size and firm size. Another way of interpreting these results is that federal funds and oil price shocks appear not to have significant complementarity or synergistic effects. iv. Robustness Checks The oil price shock results shown in Tables 1 and 2 are based on all of the lower 48 states. Some of these states have substantial oil extraction sectors and despite the use of an oil and gas extraction conditioning variable in the regressions, these states still might skew our results. Consequently, as a robustness check we excluded from our panels the four states with the largest percentage of gross state product devoted to oil and gas extraction over our sample period: Louisiana, Oklahoma, Texas, and Wyoming. The results are shown in Table 4. The same bank size pattern for personal income as in Table 2 appears: smaller bank (50 th percentile) coefficients are positive and significant, but fade and turn negative as banks become larger right across the columns. Going down the table, firm size coefficient estimates tend to be positive and significant, then fade and become negative and significant for firms with 500 or more employees. Interestingly, the adjusted R 2 s are much lower for personal income and only slightly lower for housing prices, on average for Table 4 compared to Table 2. The Federal Reserve, at the time of this writing, is employing an aggressive monetary policy that is characterized by a historically low federal funds target rate that is near zero (the zero lower bound.) Despite the zero lower bound the Federal Reserve continued to aggressively conduct monetary policy. As a robustness check, we employ an alternative measure of the federal funds rate in our VAR based 14

16 on the Taylor Rule that incorporates suggestions from Bernanke (2015) concerning its estimation. Taylor (1993) proposed the following rule for setting the nominal federal funds rate: i = real rate + inflation + 0.5(inflation - 2) + 0.5(output gap), where inflation is based on the last 4-quarter rise in the GDP deflator, 2% is the target inflation rate, and the output gap reflects the difference between the actual real GDP and the real GDP potential from the Congressional Budget Office. The rule incorporates Taylor s principle that the nominal federal funds rate should not just keep up with the inflation rate with a coefficient equal to 1.0, but should include an additional premium based on the difference between actual and target inflation to elevate the funds rate to dampen a robust economy. Bernanke (2015) suggests modifying the Taylor rule by replacing the 0.5 coefficient associated with the output gap with 1.0. Bernanke states In my experience, the FOMC paid closer attention to variants of the Taylor rule that include the higher output gap coefficient. Employing Bernanke s suggestion concerning the size of the output gap coefficient, we splice onto the actual federal funds rate the Taylor-rule-based rate from September 2008 to the fourth quarter We chose September 2008 as that date was the beginning of the Federal Reserve s extraordinary effort to stabilize the economy in the face of a zero lower bound on the federal funds rate. We reestimated Table 2 using the Taylor-rule adjusted federal funds rate with no meaningful difference compared to the results previously shown. 5 Despite the views of Cochrane (1991), Campbell and Perron (1991) and Sims, Stock, and Watson (1990), one might object to the estimation of a panel-var in levels as shown in equations (1) through (5) as several variables are likely to be nonstationary. In that model, as most VARs, we were not concerned about the statistical significance of the many individual coefficient estimates. Instead we were interested in the overall dynamics and the residuals. Nevertheless, as a robustness check, we reestimated the model in first differences. Specifically, we first-differenced state personal income, oil prices, and the consumer price index. Since the original housing prices series for each state were quarterly and we interpolated to obtain monthly estimates, we excluded housing prices from the firstdifference model. We kept the federal funds rate in levels. In the impulse regressions, we only 5 We also estimated the Taylor equation using a variety of other plausible coefficients and substituted them in the VAR with no significant difference in the regional regression results compared to those shown in this paper. Due to space constraints, the results are not shown. 15

17 included a constant and the percentage of state gross state product devoted to oil and gas extraction as our previous results shown that it is an important regional conditioning variable. The results for the first-differenced model are shown in Table 5 for accumulated personal income impulses responses from a positive oil price shock equal to one standard deviation. Because of space constraints, the estimated intercept and the estimated coefficient on the percentage of state gross state product devoted to oil and gas extraction are not shown. We present 12-month, 24-month, and 36- month forecast horizons. The interpretive methods used above apply here, and the overall results are consistent with conclusions reached using levels data. Looking across the bank size rows, there is a clear pattern for the coefficients. In the 25th and th percentiles they are positive and statistically significant then become statistically insignificant for those in the th percentile, and then negative and significant for the largest quartile. Similarly, firm size coefficients are positive and tend to be significant at the five percent level for businesses with 99 or less employees. However, they are typically insignificant for firms with employees and negative and significant for firms with 500 or more employees. The adjusted R 2 s are generally high compared to previous tables and it appears that excluding housing prices in the model does not significantly influence our previous results for personal income responses. We conclude that changes in personal income of states with a large percentage of loans held by small banks and large percentage of employment within small firms propagate oil price shocks. It is important to note that while the statistical evidence using level and first-differenced data in this paper indicate that small banks and small firms propagate oil price shocks, the economic significance of the smallest banks (assets in the 25th percentile) and smallest firms (employment less than 20) within each state is likely slight. We show, however, that somewhat larger, yet still relatively small banks and firms, also support our conclusions so that the totality of the economic impact from these groups cannot be overlooked. 16

18 IV. Conclusion The lower 48 states provide a rich data set for examining the regional differences in the effects of oil price shocks. We explore this data with state-panel VARs and their impulse responses, to gain a better understanding of how regional variation influences the propagation of shocks. We examine the model in levels and first differences, with and without high oil producing states. The main results indicate a strong and consistent pattern between personal income and housing price impulse responses and bank size and firm size. Some highlights of our findings are as follows. 1) Our slope estimates indicate the personal income responses across the states from an oil price shock are positively related to bank size for smaller banks, below the 50 th percentile. This indicates that smaller banks propagate oil price shocks through personal income. For the largest banks across the states with assets in th percentile, the slope estimates are negative and suggest that the personal income responses are negatively related to bank size. This indicates that the largest banks are also propagating oil price shocks through personal income. This finding surprised us as we expected the presence of large banks to dampen the effects of an oil shock as they are less constrained in their ability to adapt compared to smaller banks. We find no relationship between personal income impulse responses and bank size for banks the th percentile in assets across the states. 2) Housing price impulse responses are strongly related to our measures of bank size and firm size across the states based on slope estimates, but mostly in unexpected ways. Specifically, housing price impulse responses are negatively related to bank size for smaller banks (in each o the three lower quartiles) across the states. It suggests the presence of larger banks within the 75 th percentile propagate oil price shocks through housing prices. This is surprising. Housing price impulse responses and presence of large firms (500 or more employees) are negatively related over the states. This finding also surprised us as we expected the relatively high degree of flexibility of large firms to dampen or bear no relationship to shocks. The housing prices of states with a heavy presence of smaller firms are positively related to oil price shocks as expected. 17

19 Our contribution to the energy literature is that oil price shocks are propagated or dampened across the states depending on the distributions of bank size and firm size. The differences in the responses that we note in personal income and housing prices may help identify or at least provide interesting clues into the underlying dynamics. A complete explanation of these findings appears complex as there seems to be multiple channels at work. Can these results provide a link to the well-known finding of asymmetric macroeconomic responses to oil prices increases verses decreases? We look forward to gaining deeper insights into these issues in future research. 18

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21 Gertler, Mark and Simon Gilchrist, 1994, Monetary Policy, Business Cycles, and the Behavior of Small Manufacturing Firms, The Quarterly Journal of Economics, CIX, Issue 2, Hamilton James D., Herrera AM, 2004, Oil Shocks and Aggregate Economic Behavior: The Role of Monetary Policy, Journal of Money, Credit and Banking, 36: Judge, George G., R. Carter Hill, William E. Griffiths, Helmut Lutkepohl, and Tsoung-Chao Lee, 1988, Introduction to the Theory and Practice of Econometrics, second edition, John Wiley & Sons, New York. Kashyap, Anil, Owne Lamont, and Jeremy Stein, 1994, Credit Conditions and the Cyclical Behavior of Inventories: A Case Study of the Recession, Quarterly Journal of Economics, 109, Kashyap, Anil, and Jeremy Stein, 1995, The Impact of Monetary Policy on Bank Balance Sheets, Carnegie-Rochester Conference Series on Public Policy 42, Kashyap, Anil, Jeremy Stein, and David Wilcox, 1993, Monetary Policy and Credit Conditions: Evidence from the Composition of External Finance, American Economic Review, 83, Kashyap, Anil and Jeremy Stein, 2000, What Do a Million Observations Say about the Transmission of Monetary Policy? American Economic Review, 90 (3), Kilian, Lutz, 2008, The Economic Effects of Energy Price Shocks, Journal of Economic Literature, 46, Kilian, Lutz, 2014, Oil Price Shocks: Causes and Consequences, Annual Review of Resource Economics, 6, Peek, Joe and Eric S. Rosengren, 2013, The Role of Banks in the Transmission of Monetary Policy, Public Policy Discussion Papers, No. 13-5, Federal Reserve Bank of Boston. Pesaran, M. Hashem and Yongcheol Shin, 1998, Generalized Impulse Analysis in Linear Multivariate Models, Economics Letters, 58, Ravenna, Federico and, Carl E. Walsh, 2006, Optimal Monetary Policy with the Cost Channel, Journal of Monetary Economics, Volume 53, Sims, Christopher, 1980, Macroeconomics and Reality, Econometrica, 48, Sims, Christopher, James H. Stock and Mark Watson, 1990, Inference in Linear Time Seriess Models with Some Unit Roots, Econometrica, 58,

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23 Data Appendix State personal income data are from the Bureau of Economic Analysis. State housing price indexes are published by the Federal Housing Finance Agency and represent a national single-family housing price index based on data from Fannie Mae and Freddie Mac for repeat sales and refinancings on the same property, purchase price. Both personal income and housing price indices were converted to monthly data using linear interpolation methods. The federal funds rates are from the Federal Reserve St. Louis FRED website and are monthly averages. Spot crude oil prices are from the U.S. Energy Information Administration for Brent. Real potential GDP data, used to compute the output gap in the Taylor rule, are from the Congressional Budget Office. State population figures were obtained from the Bureau of Economic Analysis. State square miles are from the U.S. Geological Survey website. Firm size is categorized by the number of employees. The percentage of employment within each of these categorizes within a state data are computed from the U.S. Census Bureau s website, Statistics of U.S. Business. Data by firm size is averaged over the follow census periods: 1992, 1998, 2003, 2012, and Firm size data excludes employment from financial institutions and insurance companies. Bank size data by state comes from the FDIC website, Statistics on Depository Institutions. It captures all bank branches within a state that take deposits. State data reflects the loans and assets associated with branches located within the state. Thus, if a large money center bank had a single depositaccepting branch in a state, only the loans and assets that are associated with that branch are included in the state statistics when determining bank size. Bank size statistics reflect the percentage of loans associated with banks with assets at the indicated percentiles, derived from the lower 48 states data, within the state. These data are averaged by state and by year from 1992 to Gross state product industry data such as durable goods manufacturing is relative to the state s private industry gross state product. The government size variable is a percentage all industries, private and public, using gross state product data. The classification is NAICS, averaged from The consumer price index, All Items, from the Bureau of Labor Statistics, is used as the price deflator. 22